Constrained contextual bandit algorithm for limited-budget recommendation system

Author:

Zhao Yafei,Yang Long

Funder

Natural Science Foundation of Zhejiang Province

Peking University

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference50 articles.

1. Productization challenges of contextual multi-armed bandits;Abensur,2019

2. Agarwal, A., Hsu, D., Kale, S., Langford, J., Li, L., Schapire, R., 2014. Taming the monster: A fast and simple algorithm for contextual bandits. In: International Conference on Machine Learning. ICML, pp. 1638–1646.

3. Agrawal, S., Devanur, N.R., 2014. Bandits with concave rewards and convex knapsacks. In: Proceedings of the Fifteenth ACM Conference on Economics and Computation. EC, pp. 989–1006.

4. Linear contextual bandits with knapsacks;Agrawal,2016

5. Agrawal, S., Devanur, N.R., Li, L., 2016. An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives. In: Conference on Learning Theory. COLT, pp. 4–18.

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